import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
A						=			logging.get_logger(__name__)

A						=			{
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[int]			,       UpperCamelCase     :				Union[str, Any]			,       UpperCamelCase     :				int			,       UpperCamelCase     :				int			,       UpperCamelCase     :				Tuple							)   ->    int:
		for attribute in key.split('.'							):
				_lowerCamelCase				   =  getattr(UpperCamelCase			,       UpperCamelCase							)

		if weight_type is not None:
				_lowerCamelCase				   =  getattr(UpperCamelCase			,       UpperCamelCase							).shape
		else:
				_lowerCamelCase				   =  hf_pointer.shape

		assert hf_shape == value.shape, (
		    F"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
		    F""" {value.shape} for {full_name}"""
		)

		if weight_type == "weight":
				_lowerCamelCase				   =  value
		elif weight_type == "weight_g":
				_lowerCamelCase				   =  value
		elif weight_type == "weight_v":
				_lowerCamelCase				   =  value
		elif weight_type == "bias":
				_lowerCamelCase				   =  value
		else:
				_lowerCamelCase				   =  value

		logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""							)


def 	lowerCamelCase					(	UpperCamelCase     :				Any			,       UpperCamelCase     :				List[Any]			,       UpperCamelCase     :				Union[str, Any]							)   ->    Dict:
		_lowerCamelCase				   =  []
		_lowerCamelCase				   =  fairseq_model.state_dict()

		_lowerCamelCase				   =  hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

		for name, value in fairseq_dict.items():
				_lowerCamelCase				   =  False
				if "conv_layers" in name:
						load_conv_layer(
						    UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase			,       hf_model.config.feat_extract_norm == 'group'			,       )
						_lowerCamelCase				   =  True
				else:
						for key, mapped_key in MAPPING.items():
								_lowerCamelCase				   =  'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

								if key in name or key.split('w2v_model.'							)[-1] == name.split('.'							)[0]:
										_lowerCamelCase				   =  True
										if "*" in mapped_key:
												_lowerCamelCase				   =  name.split(UpperCamelCase							)[0].split('.'							)[-2]
												_lowerCamelCase				   =  mapped_key.replace('*'			,       UpperCamelCase							)
										if "weight_g" in name:
												_lowerCamelCase				   =  'weight_g'
										elif "weight_v" in name:
												_lowerCamelCase				   =  'weight_v'
										elif "weight" in name:
												_lowerCamelCase				   =  'weight'
										elif "bias" in name:
												_lowerCamelCase				   =  'bias'
										else:
												_lowerCamelCase				   =  None
										set_recursively(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)
								continue
				if not is_used:
						unused_weights.append(UpperCamelCase							)

		logger.warning(F"""Unused weights: {unused_weights}"""							)


def 	lowerCamelCase					(	UpperCamelCase     :				Optional[int]			,       UpperCamelCase     :				Optional[int]			,       UpperCamelCase     :				List[Any]			,       UpperCamelCase     :				int			,       UpperCamelCase     :				Optional[Any]							)   ->    str:
		_lowerCamelCase				   =  full_name.split('conv_layers.'							)[-1]
		_lowerCamelCase				   =  name.split('.'							)
		_lowerCamelCase				   =  int(items[0]							)
		_lowerCamelCase				   =  int(items[1]							)

		if type_id == 0:
				if "bias" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
						    F"""{full_name} has size {value.shape}, but"""
						    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
						)
						_lowerCamelCase				   =  value
						logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""							)
				elif "weight" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
						    F"""{full_name} has size {value.shape}, but"""
						    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
						)
						_lowerCamelCase				   =  value
						logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}."""							)
		elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
				if "bias" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
						    F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
						    " found."
						)
						_lowerCamelCase				   =  value
						logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""							)
				elif "weight" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
						    F"""{full_name} has size {value.shape}, but"""
						    F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
						)
						_lowerCamelCase				   =  value
						logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""							)
		else:
				unused_weights.append(UpperCamelCase							)


def 	lowerCamelCase					(	UpperCamelCase     :				Any			,       UpperCamelCase     :				int							)   ->    Dict:
		_lowerCamelCase				   =  SEWConfig()
		if is_finetuned:
				_lowerCamelCase				   =  model.wav_encoder.wav_model.cfg
		else:
				_lowerCamelCase				   =  model.cfg

		_lowerCamelCase				   =  fs_config.conv_bias
		_lowerCamelCase				   =  eval(fs_config.conv_feature_layers							)
		_lowerCamelCase				   =  [x[0] for x in conv_layers]
		_lowerCamelCase				   =  [x[1] for x in conv_layers]
		_lowerCamelCase				   =  [x[2] for x in conv_layers]
		_lowerCamelCase				   =  'gelu'
		_lowerCamelCase				   =  'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
		_lowerCamelCase				   =  0.0
		_lowerCamelCase				   =  fs_config.activation_fn.name
		_lowerCamelCase				   =  fs_config.encoder_embed_dim
		_lowerCamelCase				   =  0.02
		_lowerCamelCase				   =  fs_config.encoder_ffn_embed_dim
		_lowerCamelCase				   =  1e-5
		_lowerCamelCase				   =  fs_config.encoder_layerdrop
		_lowerCamelCase				   =  fs_config.encoder_attention_heads
		_lowerCamelCase				   =  fs_config.conv_pos_groups
		_lowerCamelCase				   =  fs_config.conv_pos
		_lowerCamelCase				   =  len(UpperCamelCase							)
		_lowerCamelCase				   =  fs_config.encoder_layers
		_lowerCamelCase				   =  fs_config.squeeze_factor

		# take care of any params that are overridden by the Wav2VecCtc model
		if is_finetuned:
				_lowerCamelCase				   =  model.cfg
				_lowerCamelCase				   =  fs_config.final_dropout
				_lowerCamelCase				   =  fs_config.layerdrop
		_lowerCamelCase				   =  fs_config.activation_dropout
		_lowerCamelCase				   =  fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
		_lowerCamelCase				   =  fs_config.attention_dropout
		_lowerCamelCase				   =  fs_config.dropout_input
		_lowerCamelCase				   =  fs_config.dropout
		_lowerCamelCase				   =  fs_config.mask_channel_length
		_lowerCamelCase				   =  fs_config.mask_channel_prob
		_lowerCamelCase				   =  fs_config.mask_length
		_lowerCamelCase				   =  fs_config.mask_prob

		_lowerCamelCase				   =  'Wav2Vec2FeatureExtractor'
		_lowerCamelCase				   =  'Wav2Vec2CTCTokenizer'

		return config


@torch.no_grad()
def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				Tuple			,       UpperCamelCase     :				Optional[Any]=None			,       UpperCamelCase     :				Any=None			,       UpperCamelCase     :				str=True							)   ->    Union[str, Any]:

		if is_finetuned:
				_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]			,       arg_overrides={'data': '/'.join(dict_path.split('/'							)[:-1]							)}							)
		else:
				_lowerCamelCase   ,							_lowerCamelCase   ,							_lowerCamelCase				   =  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							)

		if config_path is not None:
				_lowerCamelCase				   =  SEWConfig.from_pretrained(UpperCamelCase							)
		else:
				_lowerCamelCase				   =  convert_config(model[0]			,       UpperCamelCase							)
		_lowerCamelCase				   =  model[0].eval()

		_lowerCamelCase				   =  True if config.feat_extract_norm == 'layer' else False
		_lowerCamelCase				   =  WavaVecaFeatureExtractor(
		    feature_size=1			,       sampling_rate=1_60_00			,       padding_value=0			,       do_normalize=UpperCamelCase			,       return_attention_mask=UpperCamelCase			,       )

		if is_finetuned:
				if dict_path:
						_lowerCamelCase				   =  Dictionary.load(UpperCamelCase							)

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						_lowerCamelCase				   =  target_dict.pad_index
						_lowerCamelCase				   =  target_dict.bos_index
						_lowerCamelCase				   =  target_dict.pad_index
						_lowerCamelCase				   =  target_dict.bos_index
						_lowerCamelCase				   =  target_dict.eos_index
						_lowerCamelCase				   =  len(target_dict.symbols							)
						_lowerCamelCase				   =  os.path.join(UpperCamelCase			,       'vocab.json'							)
						if not os.path.isdir(UpperCamelCase							):
								logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(UpperCamelCase							)							)
								return
						os.makedirs(UpperCamelCase			,       exist_ok=UpperCamelCase							)
						with open(UpperCamelCase			,       'w'			,       encoding='utf-8'							) as vocab_handle:
								json.dump(target_dict.indices			,       UpperCamelCase							)
						_lowerCamelCase				   =  WavaVecaCTCTokenizer(
						    UpperCamelCase			,       unk_token=target_dict.unk_word			,       pad_token=target_dict.pad_word			,       bos_token=target_dict.bos_word			,       eos_token=target_dict.eos_word			,       word_delimiter_token='|'			,       do_lower_case=UpperCamelCase			,       )
						_lowerCamelCase				   =  WavaVecaProcessor(feature_extractor=UpperCamelCase			,       tokenizer=UpperCamelCase							)
						processor.save_pretrained(UpperCamelCase							)

				_lowerCamelCase				   =  SEWForCTC(UpperCamelCase							)
		else:
				_lowerCamelCase				   =  SEWModel(UpperCamelCase							)
				feature_extractor.save_pretrained(UpperCamelCase							)

		recursively_load_weights(UpperCamelCase			,       UpperCamelCase			,       UpperCamelCase							)

		hf_model.save_pretrained(UpperCamelCase							)


if __name__ == "__main__":
				A						=			argparse.ArgumentParser()
				parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
				parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
				parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
				parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
				parser.add_argument(
				    '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
				)
				A						=			parser.parse_args()
				convert_sew_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
				)
def 	lowerCamelCase					(	UpperCamelCase     :				int			,       UpperCamelCase     :				int							)   ->    float:
		return base * power(UpperCamelCase			,       (exponent - 1)							) if exponent else 1


if __name__ == "__main__":
				print('Raise base to the power of exponent using recursion...')
				A						=			int(input('Enter the base: ').strip())
				A						=			int(input('Enter the exponent: ').strip())
				A						=			power(base, abs(exponent))
				if exponent < 0:  # power() does not properly deal w/ negative exponents
								A						=			1 / result
				print(F'''{base} to the power of {exponent} is {result}''')